This study examines the development of the digital economy and society in the Republic of Kazakhstan by combining international benchmarking with a context-specific national framework. It highlights the limitations of existing global indices such as DESI, NRI, and EGDI in capturing the structural and institutional dimensions of digital transformation in emerging economies. To address this gap, the study introduces a novel composite metric, the Index of Digital Economy Development (IDED), which integrates five sub-indices: infrastructure, usage, human capital, economic digitization, and transformation effectiveness. The methodology involves comparative index analysis, the construction of the IDED, and statistical validation through a public opinion survey and regression modeling. Key findings indicate that cybersecurity is a critical yet under-represented component of digital development, showing strong empirical correlations with DESI scores in benchmark countries. The results also highlight Kazakhstan’s strengths in digital public services and internet access, contrasted with weaknesses in business digitization and innovation. The proposed IDED offers a more comprehensive and policy-relevant tool for assessing digital progress in transitional economies. This study contributes to the literature by proposing a replicable index structure and providing empirical evidence for the inclusion of cybersecurity in national digital economy assessments. The aim of the study is to assess Kazakhstan’s digital economy development by addressing limitations in global measurement frameworks. Methodologically, it combines comparative index analysis, the construction of a national composite index (IDED), and statistical validation using a regional survey and regression analysis. The findings reveal both strengths and gaps in Kazakhstan’s digital landscape, particularly in cybersecurity and SME digitalization. The IDED introduces an innovative, context-sensitive framework that enhances the measurement of digital transformation in transitional economies.

Deep learning (DL) has emerged as a fast expanding field of study in recent decades, redefining the state-of-the-art in a variety of methods, including speech recognition and object detection. Many projects in the fields of aircraft design, behaviour, and control rely on the extensive data-driven approach. These projects include the development of flight control systems, intelligent sensing, fusion-based prognosis and health management, and airliner flight safety monitoring. Aerodynamic nonlinearities, outside influences, and parameter fluctuations result in highly nonlinear, unpredictable, and time-varying dynamics for modern aerospace vehicles. Traditional robust and adaptive control methods frequently rely on permanent structures and simple models, which might restrict performance in situations where flying conditions change quickly. A deep learning-based adaptive flight control structure for nonlinear aircraft systems that learns and adjusts for unknown dynamic in real time is presented in this research. While an adaptive control rule guarantees closed-loop safety and trajectory tracking performance, a deep neural network is used to simulate modelling uncertainties and unmodeled nonlinearities. A nonlinear aeroplane model is used to test the suggested method under various aerodynamic circumstances and outside disruptions. The potential of machine learning for next-generation smart flight control systems is highlighted by simulation findings that show increased tracking accuracy, resilience, and flexibility when compared to conventional model-based adaptive controllers.